Comparing Bias Correction Methods Used in Downscaling Precipitation and Temperature from Regional Climate Models: A Case Study from the Kaidu River Basin in Western China

Abstract

Abstract:The systemic biases of Regional Climate Models (RCMs) impede their application in regional hydrological climate-change effects analysis and lead to errors. As a consequence, bias correction has become a necessary prerequisite for the study of climate change. This paper compares the performance of available bias correction methods that focus on the performance of precipitation and temperature projections. The hydrological effects of these correction methods are evaluated by the modelled discharges of the Kaidu River Basin. The results show that all used methods improve the performance of the original RCM precipitation and temperature simulations across a number of levels. The corrected results obtained by precipitation correction methods demonstrate larger diversities than those produced by the temperature correction methods. The performance of hydrological modelling is highly influenced by the choice of precipitation correction methods. Furthermore, no substantial differences can be identified from the results of the temperature-corrected methods. The biases from input data are often greater from the works of hydrological modelling. The suitability of these approaches depends upon the regional context and the RCM model, while their application procedure and a number of results can be adapted from region to region.

Emptying Water Towers? Impacts of Future Climate and Glacier Change on River Discharge in the Northern Tien Shan, Central Asia

“…Figure 3 presents a comparison of mean monthly temperature values the simulation driven by ECHAM 5 model. The EQM and VARI methods performed best in the correction of temperature projections in this study in line with previous comparisons for western China [43]. The EQM-corrected data were selected to force the hydrological model as this method showed a marginal advantage over the VARI method.…”

“…The HO 3-D model is coupled to a surface mass balance model based on the energybalance approach [47][48][49], which simulates spatial and temporal distribution of accumulation and ablation. The models operate on a grid with a spatial resolution of 25 m. The HO 3-D model has a temporal time step of approximately 1 week (0.02 year) while mass balance is updated annually at The published comparisons of the performance of these methods [39,43] applied to precipitation suggest that QM and PT methods perform best in correcting frequency distributions in the modelled data while LOCI results in a closer match between the modelled and observed records with higher coefficients of determination and Nash-Sutcliffe Efficiency (NSE) index. These methods, however, are mostly aimed at the correction of bias associated with frequency and intensity of wet days.…”

Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

“…Under the present configuration, LS also uses unique scaling factors during specific months where no extreme precipitation is specifically considered, often leading to heavy precipitation being greatly underestimated [52,112]. Similar to JJA, GPCC clearly benefits from bias correction mainly for the Central-Valley region, with most BC rendering similar performances.…”

“…The superior performance of EQM could also be attributable to the adopted monthly approach used in the BC methods application, which takes different precipitation levels into account on individual basis. Various bias correction evaluation studies have also concluded that EQM is typically superior when compared to other BC methods [21,22,32,50,52,53,55,87,[106][107][108]. Contrarily, the distribution-based parametric methods (GQM and GPQM) show slightly larger bias when compared to empirical distributions (EQM), as these methods are based on the assumption that both GCM-RCM and observed data approximate the corresponding theoretical distributions functions [29,35], which could not always be the case for the temporal and spatial precipitation distribution within most climatic regions, especially during the dry season.…”

Selection of Effective GCM Bias Correction Methods and Evaluation of Hydrological Response under Future Climate Scenarios

“…Many researchers have evaluated the performances of different bias correction methods. For example, Luo et al [16] compared the effects of LS, DT (Daily Transition), LOCI (Local Intensity Scaling), PT, VARI (Variance Scaling), and ECDF methods of either precipitation or temperature in the Kaidu River Basin in Xinjiang, China, and found that ECDF performs better than other methods. Teutschbein et al [17] also made the introduction and comparison of these different correction methods in Sweden, and also found that all the methods are effective, while distribution mapping is of relatively more success.…”